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Adapting forest management to climate change using bioclimate models with topographic driversAuthor(s): Gerald E. Rehfeldt; James J. Worrall; Suzanne B. Marchetti; Nicholas L. Crookston
Source: Forestry. doi: 10.1093/forestry/cpv019.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
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DescriptionBioclimate models incorporating topographic predictors as surrogates for microclimate effects are developed for Populus tremuloides and Picea engelmannii to provide the fine-grained specificity to local terrain required for adapting management of three Colorado (USA) national forests (1.28 million ha) and their periphery to climate change. Models were built with the Random Forests classification tree using presence - absence observations obtained by overlaying species distribution maps on data points gridded at ~225 m within the forests and from ground plot observations from adjacent areas. Topographic effects derived from 90-m elevation grids were expressed by weighting aspect by slope angle. Climate estimates were obtained from spline surfaces. Out-of-bag errors were ~17 per cent, and classification errors for an independent sample from within the forest were ~13 per cent. Topographic variables were second in importance to climate variables for predicting species distributions; their inclusion captured well-known topographic effects on vegetation in mountainous terrain. Predictions made for future climates described by three General Circulation Models and three emissions scenarios were used to map on 90-m grids the habitat expected to be lost, threatened, persistent or emergent. The habitat categories are used to identify those areas where treatments should have highest likelihood of success.
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CitationRehfeldt, Gerald E.; Worrall, James J.; Marchetti, Suzanne B.; Crookston, Nicholas L. 2015. Adapting forest management to climate change using bioclimate models with topographic drivers. Forestry. doi: 10.1093/forestry/cpv019.
Keywordsforest management, climate change, bioclimate models, topographic variables
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